The Principal Component Analysis or PCA is a statistical technique for reducing the dimension of a large dataset. It does it by transforming the dataset into a new set of variables, the principal components, which are uncorrelated, linear combinations of the initial variables and represent the most important part of the variability of the data. […]
The valuation and the risk management of options can be quickly complex. It depends on the option’s feature and the pricing model. There is often no closed-form solution for the pricing of the derivatives and it involves multiple dimensions. There is a vaste litterature on numerical methods such as binomial / trinomial tree, finite difference, […]
What is it? How does it work? Principal Component Analysis (PCA) is a statistical method for reducing the dimension of a dataset. It is a popular method for analysing a large dataset, increasing the interpretability of the data without losing much information. It does that by maximizing the percentage of total variance explained with new […]
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